Detection of nanoparticles in mice using an integrated photoacoustic micro‐ultrasound system
Bibliographic record
Abstract
VisualSonics have recently developed a photoacoustic imaging system (VevoLAZR, Toronto, Canada) that combines the sensitivity of optical imaging modalities and the high resolution of micro‐ultrasound. The high sensitivity enables detection of nano‐scaled contrast agents. Due to their small size, nanoparticles likely can cross the leaky tumor microvasculature and deposit in the extra‐ and intracellular space. This presents countless potential benefits in cancer therapy. Using VevoLAZR we investigated the use of various gold nanoparticles and common optical fluorescent dyes as photoacoustic contrast agents in a vessel phantom study. We also injected gold nanorods intravenously in mice bearing tumors and investigated the preferential accumulation of these nanoparticles in the tumor tissue. Tumor perfusion was also quantified using ultrasound contrast imaging. Gold nanorods showed the greatest photoacoustic intensity amongst the nanoparticles tested. Optical fluorescent dyes used were also readily detectable in photoacoustic imaging. In the tumor study, preferential accumulation of gold nanorods was observed in the tumor relative to mammary tissue. Interestingly, tumor perfusion appeared to be inversely correlated with amount of gold nanorod accumulation. Collectively, we have demonstrated photoacoustics as a novel and powerful tool to detect and quantify nanoparticles in mice tumors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".